# --------------------------------------------------------
# Swin Transformer
# Copyright (c) 2021 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ze Liu
# Modified by Zhenda Xie
# --------------------------------------------------------

from unittest import result
import torch
if torch.__version__ >= '1.8':
    import torch_npu
import torch.nn as nn
import torch.utils.checkpoint as checkpoint
from timm.models.layers import DropPath, to_2tuple, trunc_normal_
from apex import amp


"""
LayerNorm is able to run on CUBE in some cases for high-performance.
To avoid unnecessary format transformation, a blacklist is set here.

TODO: create blacklist automatically
"""
_LAYERNORM_FORMAT_NZ = False # To accelerate if layernorm in fp16, but acc not ok here
_LAYERNORM_FORMAT_NZ_BLACKLIST = {192, 384, 768, 1536}

class FastGELU(nn.Module):
    """fast version of nn.GELU()"""

    @staticmethod
    def forward(x):
        return torch_npu.fast_gelu(x)


def npu_drop_path(x, random_tensor, keep_prob: float = 0.):
    """
    Less ops than timm version.
    Async generating and applying of random tensor for accelerating.
    """
    random_tensor += keep_prob
    random_tensor.floor_()  # binarize
    output = x.div(keep_prob) * random_tensor
    return output


class DropPathTask:
    def __init__(self, shape, device, dtype, ndim, drop_prob):
        self.shape = shape
        self.device = device
        self.dtype = dtype
        self.ndim = ndim
        self.drop_prob = drop_prob

        self.request_count = 0
        self.rand_queue = []


class NpuDropPath(nn.Module):
    """Drop paths (Stochastic Depth) per sample  (when applied in main path of residual blocks.)
    """
    task_dict = {}
    droppath_stream = None

    def __init__(self, drop_prob=None):
        super(NpuDropPath, self).__init__()
        self.drop_prob = drop_prob
        self.keep_prob = 1 - drop_prob

    def forward(self, x):
        if self.drop_prob == 0. or not self.training:
            return x

        if isinstance(x, torch.Tensor):
            shape = x.shape
            dtype = x.dtype
            device = x.device
            ndim = x.ndim
        else:
            raise RuntimeError("input type error!")

        key = (shape, device, dtype, ndim)
        if key not in NpuDropPath.task_dict:
            droppath_task = DropPathTask(shape, device, dtype, ndim, self.drop_prob)
            droppath_task.request_count += 1
            NpuDropPath.task_dict[key] = droppath_task
        elif not NpuDropPath.task_dict[key].rand_queue:
            NpuDropPath.task_dict[key].request_count += 1
        else:
            random_tensor = NpuDropPath.task_dict[key].rand_queue.pop(0)
            return npu_drop_path(x, random_tensor, self.keep_prob)

        return x

    @classmethod
    def enable_droppath_ensemble(cls, model):
        if cls.droppath_stream is None:
            cls.droppath_stream = torch.npu.Stream()

        def wait_stream_hook():
            def hook_function(module, inputs):
                torch.npu.current_stream().wait_stream(cls.droppath_stream)
            return hook_function
        model.register_forward_pre_hook(wait_stream_hook())

        def random_tensor_gen_hook():
            def hook_function(module, inputs):
                with torch.npu.stream(cls.droppath_stream):
                    with torch.no_grad():
                        for _, task in cls.task_dict.items():
                            if len(task.rand_queue) >= task.request_count:
                                continue
                            for i in range(task.request_count - len(task.rand_queue)):
                                shape = (task.shape[0],) + (1,) * (task.ndim - 1)  # work with diff dim tensors, not just 2D ConvNets
                                random_tensor = torch.rand(shape, dtype=task.dtype, device=task.device)
                                task.rand_queue.append(random_tensor)
            return hook_function
        model.register_forward_pre_hook(random_tensor_gen_hook())

class MatmulApply(torch.autograd.Function):
    @staticmethod
    def forward(ctx, self, mat2):
        # y = a * b^T
        ctx.save_for_backward(self, mat2)
        result = torch.matmul(self, mat2.transpose(-2, -1))
        return result
    @staticmethod
    def backward(ctx, grad):
        # da: grad * b
        # db: grad^T * a
        self, mat2 = ctx.saved_tensors
        self_grad = torch_npu.npu_bmmV2(grad, mat2, [])
        mat2_grad = torch_npu.npu_bmmV2(grad.transpose(-2, -1), self, [])
        return self_grad, mat2_grad

matmul_transpose = MatmulApply.apply

class RollIndexSelect(torch.autograd.Function):
    @staticmethod
    def forward(ctx, input, index_fp, index_bp):
        N, H, W, C = input.shape
        ctx.input = input
        ctx.index_bp = index_bp
        result = input.reshape(N, H * W, C).index_select(1, index_fp).reshape(N, H, W, C)
        return result
    @staticmethod
    def backward(ctx, grad):
        input = ctx.input
        N, H, W, C = input.shape
        index_bp = ctx.index_bp
        grad_input = grad.reshape(N, H * W, C).index_select(1, index_bp).reshape(N, H, W, C)
        return grad_input, None, None

roll_index_select = RollIndexSelect.apply

class Mlp(nn.Module):
    def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=FastGELU, drop=0.):
        super().__init__()
        out_features = out_features or in_features
        hidden_features = hidden_features or in_features
        self.fc1 = nn.Linear(in_features, hidden_features)
        self.act = act_layer()
        self.fc2 = nn.Linear(hidden_features, out_features)
        self.drop = nn.Dropout(drop) if drop > 0. else nn.Identity()

    def forward(self, x):
        x = self.fc1(x)
        x = self.act(x)
        x = self.drop(x)
        x = self.fc2(x)
        x = self.drop(x)
        return x


def window_partition(x, window_size):
    """
    Args:
        x: (B, H, W, C)
        window_size (int): window size

    Returns:
        windows: (num_windows*B, window_size, window_size, C)
    """
    B, H, W, C = x.shape
    x = x.view(B, H // window_size, window_size, W // window_size, window_size, C)
    windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
    return windows


def window_reverse(windows, window_size, H, W):
    """
    Args:
        windows: (num_windows*B, window_size, window_size, C)
        window_size (int): Window size
        H (int): Height of image
        W (int): Width of image

    Returns:
        x: (B, H, W, C)
    """
    B_, H_, W_, C_ = windows.shape

    B = int(windows.shape[0] / (H * W / window_size / window_size))
    x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1)

    # x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
    C = int((B_ * H_ * W_ * C_) / (B * H * W))
    x = torch_npu.npu_confusion_transpose(x, [0, 1, 3, 2, 4, 5], (B, H, W, C), True)

    return x


class WindowAttention(nn.Module):
    r""" Window based multi-head self attention (W-MSA) module with relative position bias.
    It supports both of shifted and non-shifted window.

    Args:
        dim (int): Number of input channels.
        window_size (tuple[int]): The height and width of the window.
        num_heads (int): Number of attention heads.
        qkv_bias (bool, optional):  If True, add a learnable bias to query, key, value. Default: True
        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set
        attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0
        proj_drop (float, optional): Dropout ratio of output. Default: 0.0
    """

    def __init__(self, dim, window_size, num_heads, qkv_bias=True, qk_scale=None, attn_drop=0., proj_drop=0.):

        super().__init__()
        self.dim = dim
        self.window_size = window_size  # Wh, Ww
        self.num_heads = num_heads
        head_dim = dim // num_heads
        self.scale = torch.tensor(qk_scale) if qk_scale else torch.tensor(head_dim ** -0.5)

        # define a parameter table of relative position bias
        self.relative_position_bias_table = nn.Parameter(
            torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads))  # 2*Wh-1 * 2*Ww-1, nH

        # get pair-wise relative position index for each token inside the window
        coords_h = torch.arange(self.window_size[0])
        coords_w = torch.arange(self.window_size[1])
        coords = torch.stack(torch.meshgrid([coords_h, coords_w]))  # 2, Wh, Ww
        coords_flatten = torch.flatten(coords, 1)  # 2, Wh*Ww
        relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :]  # 2, Wh*Ww, Wh*Ww
        relative_coords = relative_coords.permute(1, 2, 0).contiguous()  # Wh*Ww, Wh*Ww, 2
        relative_coords[:, :, 0] += self.window_size[0] - 1  # shift to start from 0
        relative_coords[:, :, 1] += self.window_size[1] - 1
        relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1
        relative_position_index = relative_coords.sum(-1).view(-1).clone()  # Wh*Ww, Wh*Ww
        self.register_buffer("relative_position_index", relative_position_index)

        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
        self.attn_drop = nn.Dropout(attn_drop) if attn_drop > 0. else nn.Identity()
        self.proj = nn.Linear(dim, dim)
        self.proj_drop = nn.Dropout(proj_drop) if proj_drop > 0. else nn.Identity()

        trunc_normal_(self.relative_position_bias_table, std=.02)
        self.softmax = nn.Softmax(dim=-1)

    @amp.half_function
    def forward(self, x, mask=None):
        """
        Args:
            x: input features with shape of (num_windows*B, N, C)
            mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None
        """
        B_, N, C = x.shape
        qkv = torch_npu.npu_format_cast(
            self.qkv(x).reshape(B_, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4).contiguous(),
            2
        )
        q, k, v = qkv[0].clone(), qkv[1].clone(), qkv[2].clone()  # make torchscript happy (cannot use tensor as tuple)

        if not self.scale.device == q.device:
            self.scale = self.scale.to(q.device).to(q.dtype)

        q = q * self.scale
        # attn = (q @ k.transpose(-2, -1))
        attn = matmul_transpose(q, k)

        # relative_position_bias = self.relative_position_bias_table[self.relative_position_index.view(-1)].view(
        #     self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1)  # Wh*Ww,Wh*Ww,nH
        relative_position_bias = torch.index_select(self.relative_position_bias_table, 0, self.relative_position_index).view(
            self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1)
        relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous()  # nH, Wh*Ww, Wh*Ww
        if attn.dtype == torch.float16:
            attn = attn + relative_position_bias.unsqueeze(0).half()
        else:
            attn = attn + relative_position_bias.unsqueeze(0)

        if mask is not None:
            nW = mask.shape[0]
            attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0)
            attn = attn.view(-1, self.num_heads, N, N)
            attn = self.softmax(attn)
        else:
            attn = self.softmax(attn)

        attn = self.attn_drop(attn)

        # x = (attn @ v).transpose(1, 2).reshape(B_, N, C)
        x = torch_npu.npu_confusion_transpose(torch_npu.npu_format_cast((attn @ v), 2), [0, 2, 1, 3], (B_, N, C), True)
        x = self.proj(x)
        x = self.proj_drop(x)
        return x

    def extra_repr(self) -> str:
        return f'dim={self.dim}, window_size={self.window_size}, num_heads={self.num_heads}'

    def flops(self, N):
        # calculate flops for 1 window with token length of N
        flops = 0
        # qkv = self.qkv(x)
        flops += N * self.dim * 3 * self.dim
        # attn = (q @ k.transpose(-2, -1))
        flops += self.num_heads * N * (self.dim // self.num_heads) * N
        #  x = (attn @ v)
        flops += self.num_heads * N * N * (self.dim // self.num_heads)
        # x = self.proj(x)
        flops += N * self.dim * self.dim
        return flops


def get_roll_index(H, W, shifts):
    index = torch.arange(0, H * W).reshape(H, W)
    index_fp = torch.roll(index, shifts=(shifts, shifts), dims=(0, 1)).reshape(-1).long()
    index_bp = {i:idx for idx, i in enumerate(index_fp.numpy().tolist())}
    index_bp = [index_bp[i] for i in range(H * W)]
    index_bp = torch.LongTensor(index_bp)
    return [index_fp, index_bp]

class SwinTransformerBlock(nn.Module):
    r""" Swin Transformer Block.

    Args:
        dim (int): Number of input channels.
        input_resolution (tuple[int]): Input resulotion.
        num_heads (int): Number of attention heads.
        window_size (int): Window size.
        shift_size (int): Shift size for SW-MSA.
        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
        drop (float, optional): Dropout rate. Default: 0.0
        attn_drop (float, optional): Attention dropout rate. Default: 0.0
        drop_path (float, optional): Stochastic depth rate. Default: 0.0
        act_layer (nn.Module, optional): Activation layer. Default: nn.GELU
        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
    """

    def __init__(self, dim, input_resolution, num_heads, window_size=7, shift_size=0,
                 mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0., drop_path=0.,
                 act_layer=FastGELU, norm_layer=nn.LayerNorm, norm_before_mlp='ln'):
        super().__init__()
        self.dim = dim
        self.input_resolution = input_resolution
        self.num_heads = num_heads
        self.window_size = window_size
        self.shift_size = shift_size
        self.mlp_ratio = mlp_ratio
        self.norm_before_mlp = norm_before_mlp
        if min(self.input_resolution) <= self.window_size:
            # if window size is larger than input resolution, we don't partition windows
            self.shift_size = 0
            self.window_size = min(self.input_resolution)
        assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size"

        self.norm1 = norm_layer(dim)
        self.attn = WindowAttention(
            dim, window_size=to_2tuple(self.window_size), num_heads=num_heads,
            qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)

        self.drop_path = NpuDropPath(drop_path) if drop_path > 0. else nn.Identity()
        if self.norm_before_mlp == 'ln':
            self.norm2 = nn.LayerNorm(dim)
        elif self.norm_before_mlp == 'bn':
            self.norm2 = lambda x: nn.BatchNorm1d(dim)(x.transpose(1, 2)).transpose(1, 2)
        else:
            raise NotImplementedError
        mlp_hidden_dim = int(dim * mlp_ratio)
        self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)

        if self.shift_size > 0:
            # calculate attention mask for SW-MSA
            H, W = self.input_resolution
            img_mask = torch.zeros((1, H, W, 1))  # 1 H W 1
            h_slices = (slice(0, -self.window_size),
                        slice(-self.window_size, -self.shift_size),
                        slice(-self.shift_size, None))
            w_slices = (slice(0, -self.window_size),
                        slice(-self.window_size, -self.shift_size),
                        slice(-self.shift_size, None))
            cnt = 0
            for h in h_slices:
                for w in w_slices:
                    img_mask[:, h, w, :] = cnt
                    cnt += 1

            mask_windows = window_partition(img_mask, self.window_size)  # nW, window_size, window_size, 1
            mask_windows = mask_windows.view(-1, self.window_size * self.window_size)
            attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
            attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0))
        else:
            attn_mask = None

        self.register_buffer("attn_mask", attn_mask)

        self.index_dict = {}
        self.index_device = torch.device('cpu')
        hw_list = [56, 28, 14, 7] # H/W of feature maps
        for hw in hw_list:
            H, W = hw, hw
            self.index_dict[(H, W, self.shift_size)] = get_roll_index(H, W, self.shift_size)
            self.index_dict[(H, W, -self.shift_size)] = get_roll_index(H, W, -self.shift_size)

    def cast_index_device(self, device):
        for v in self.index_dict.values():
            v[0] = v[0].to(device)
            v[1] = v[1].to(device)

    @amp.half_function
    def forward(self, x):
        if not self.index_device == x.device:
            self.cast_index_device(x.device)
            self.index_device = x.device

        H, W = self.input_resolution
        B, L, C = x.shape
        assert L == H * W, "input feature has wrong size"

        shortcut = x
        if _LAYERNORM_FORMAT_NZ and x.size(-1) not in _LAYERNORM_FORMAT_NZ_BLACKLIST:
            x = torch_npu.npu_format_cast(x, 29)
        x = self.norm1(x)
        x = torch_npu.npu_format_cast(x.view(B, H, W, C), 2)

        # cyclic shift
        if self.shift_size > 0:
            # shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2))
            index_fp = self.index_dict[(H, W, -self.shift_size)][0]
            index_bp = self.index_dict[(H, W, -self.shift_size)][1]
            shifted_x = roll_index_select(x, index_fp, index_bp)
        else:
            shifted_x = x

        # partition windows
        x_windows = window_partition(shifted_x, self.window_size)  # nW*B, window_size, window_size, C
        x_windows = x_windows.view(-1, self.window_size * self.window_size, C)  # nW*B, window_size*window_size, C

        # W-MSA/SW-MSA
        attn_windows = self.attn(x_windows, mask=self.attn_mask)  # nW*B, window_size*window_size, C

        # merge windows
        attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C)
        shifted_x = window_reverse(attn_windows, self.window_size, H, W)  # B H' W' C

        # reverse cyclic shift
        if self.shift_size > 0:
            # x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2))
            index_fp = self.index_dict[(H, W, self.shift_size)][0]
            index_bp = self.index_dict[(H, W, self.shift_size)][1]
            x = roll_index_select(shifted_x, index_fp, index_bp)
        else:
            x = shifted_x
        x = x.view(B, H * W, C)

        # FFN
        x = shortcut + self.drop_path(x)

        # x = x + self.drop_path(self.mlp(self.norm2(x)))
        if _LAYERNORM_FORMAT_NZ and x.size(-1) not in _LAYERNORM_FORMAT_NZ_BLACKLIST:
            x = x + self.drop_path(self.mlp(self.norm2(torch_npu.npu_format_cast(x, 29))))
        else:
            x = x + self.drop_path(self.mlp(self.norm2(x)))
        return x

    def extra_repr(self) -> str:
        return f"dim={self.dim}, input_resolution={self.input_resolution}, num_heads={self.num_heads}, " \
               f"window_size={self.window_size}, shift_size={self.shift_size}, mlp_ratio={self.mlp_ratio}"

    def flops(self):
        flops = 0
        H, W = self.input_resolution
        # norm1
        flops += self.dim * H * W
        # W-MSA/SW-MSA
        nW = H * W / self.window_size / self.window_size
        flops += nW * self.attn.flops(self.window_size * self.window_size)
        # mlp
        flops += 2 * H * W * self.dim * self.dim * self.mlp_ratio
        # norm2
        flops += self.dim * H * W
        return flops


class PatchMerging(nn.Module):
    r""" Patch Merging Layer.

    Args:
        input_resolution (tuple[int]): Resolution of input feature.
        dim (int): Number of input channels.
        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
    """

    def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm):
        super().__init__()
        self.input_resolution = input_resolution
        self.dim = dim
        self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
        self.norm = norm_layer(4 * dim)

    @amp.half_function
    def forward(self, x):
        """
        x: B, H*W, C

        A depth-wise conv2d version with save semantics of following op
        # x0 = x[:, 0::2, 0::2, :]  # B H/2 W/2 C
        # x1 = x[:, 1::2, 0::2, :]  # B H/2 W/2 C
        # x2 = x[:, 0::2, 1::2, :]  # B H/2 W/2 C
        # x3 = x[:, 1::2, 1::2, :]  # B H/2 W/2 C
        # x = torch.cat([x0, x1, x2, x3], -1)  # B H/2 W/2 4*C
        """

        H, W = self.input_resolution
        B, L, C = x.shape
        assert L == H * W, "input feature has wrong size"
        assert H % 2 == 0 and W % 2 == 0, f"x size ({H}*{W}) are not even."

        x = x.reshape(B, int(H / 2), 2, int(W / 2), 2, C)
        x = x.permute(0, 1, 3, 4, 2, 5)
        x = x.reshape(B, int(H * W / 4), C * 4)

        if _LAYERNORM_FORMAT_NZ and x.size(-1) not in _LAYERNORM_FORMAT_NZ_BLACKLIST:
            x = torch_npu.npu_format_cast(torch_npu.npu_format_cast(x, 2), 29).contiguous()

        x = self.norm(x)
        x = self.reduction(x)

        return x

    def extra_repr(self) -> str:
        return f"input_resolution={self.input_resolution}, dim={self.dim}"

    def flops(self):
        H, W = self.input_resolution
        flops = H * W * self.dim
        flops += (H // 2) * (W // 2) * 4 * self.dim * 2 * self.dim
        return flops


class BasicLayer(nn.Module):
    """ A basic Swin Transformer layer for one stage.

    Args:
        dim (int): Number of input channels.
        input_resolution (tuple[int]): Input resolution.
        depth (int): Number of blocks.
        num_heads (int): Number of attention heads.
        window_size (int): Local window size.
        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
        drop (float, optional): Dropout rate. Default: 0.0
        attn_drop (float, optional): Attention dropout rate. Default: 0.0
        drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
        norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
        downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
    """

    def __init__(self, dim, input_resolution, depth, num_heads, window_size,
                 mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0.,
                 drop_path=0., norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False,
                 norm_before_mlp='ln'):

        super().__init__()
        self.dim = dim
        self.input_resolution = input_resolution
        self.depth = depth
        self.use_checkpoint = use_checkpoint

        # build blocks
        self.blocks = nn.ModuleList([
            SwinTransformerBlock(dim=dim, input_resolution=input_resolution,
                                 num_heads=num_heads, window_size=window_size,
                                 shift_size=0 if (i % 2 == 0) else window_size // 2,
                                 mlp_ratio=mlp_ratio,
                                 qkv_bias=qkv_bias, qk_scale=qk_scale,
                                 drop=drop, attn_drop=attn_drop,
                                 drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
                                 norm_layer=norm_layer, norm_before_mlp=norm_before_mlp)
            for i in range(depth)])

        # patch merging layer
        if downsample is not None:
            self.downsample = downsample(input_resolution, dim=dim, norm_layer=norm_layer)
        else:
            self.downsample = None

    def forward(self, x):
        for blk in self.blocks:
            if self.use_checkpoint:
                x = checkpoint.checkpoint(blk, x)
            else:
                x = blk(x)
        if self.downsample is not None:
            x = self.downsample(x)
        return x

    def extra_repr(self) -> str:
        return f"dim={self.dim}, input_resolution={self.input_resolution}, depth={self.depth}"

    def flops(self):
        flops = 0
        for blk in self.blocks:
            flops += blk.flops()
        if self.downsample is not None:
            flops += self.downsample.flops()
        return flops


class PatchEmbed(nn.Module):
    r""" Image to Patch Embedding

    Args:
        img_size (int): Image size.  Default: 224.
        patch_size (int): Patch token size. Default: 4.
        in_chans (int): Number of input image channels. Default: 3.
        embed_dim (int): Number of linear projection output channels. Default: 96.
        norm_layer (nn.Module, optional): Normalization layer. Default: None
    """

    def __init__(self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None):
        super().__init__()
        img_size = to_2tuple(img_size)
        patch_size = to_2tuple(patch_size)
        patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]]
        self.img_size = img_size
        self.patch_size = patch_size
        self.patches_resolution = patches_resolution
        self.num_patches = patches_resolution[0] * patches_resolution[1]

        self.in_chans = in_chans
        self.embed_dim = embed_dim

        self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
        if norm_layer is not None:
            self.norm = norm_layer(embed_dim)
        else:
            self.norm = None

    def forward(self, x):
        B, C, H, W = x.shape
        # FIXME look at relaxing size constraints
        assert H == self.img_size[0] and W == self.img_size[1], \
            f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})."
        x = self.proj(x).flatten(2).transpose(1, 2).contiguous()  # B Ph*Pw C
        if self.norm is not None:
            if _LAYERNORM_FORMAT_NZ and x.size(-1) not in _LAYERNORM_FORMAT_NZ_BLACKLIST:
                x = torch_npu.npu_format_cast(torch_npu.npu_format_cast(x, 2), 29)
            x = self.norm(x)
        return x

    def flops(self):
        Ho, Wo = self.patches_resolution
        flops = Ho * Wo * self.embed_dim * self.in_chans * (self.patch_size[0] * self.patch_size[1])
        if self.norm is not None:
            flops += Ho * Wo * self.embed_dim
        return flops


class SwinTransformer(nn.Module):
    r""" Swin Transformer
        A PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows`  -
          https://arxiv.org/pdf/2103.14030

    Args:
        img_size (int | tuple(int)): Input image size. Default 224
        patch_size (int | tuple(int)): Patch size. Default: 4
        in_chans (int): Number of input image channels. Default: 3
        num_classes (int): Number of classes for classification head. Default: 1000
        embed_dim (int): Patch embedding dimension. Default: 96
        depths (tuple(int)): Depth of each Swin Transformer layer.
        num_heads (tuple(int)): Number of attention heads in different layers.
        window_size (int): Window size. Default: 7
        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4
        qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True
        qk_scale (float): Override default qk scale of head_dim ** -0.5 if set. Default: None
        drop_rate (float): Dropout rate. Default: 0
        attn_drop_rate (float): Attention dropout rate. Default: 0
        drop_path_rate (float): Stochastic depth rate. Default: 0.1
        norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.
        ape (bool): If True, add absolute position embedding to the patch embedding. Default: False
        patch_norm (bool): If True, add normalization after patch embedding. Default: True
        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False
    """

    def __init__(self, img_size=224, patch_size=4, in_chans=3, num_classes=1000,
                 embed_dim=96, depths=[2, 2, 6, 2], num_heads=[3, 6, 12, 24],
                 window_size=7, mlp_ratio=4., qkv_bias=True, qk_scale=None,
                 drop_rate=0., attn_drop_rate=0., drop_path_rate=0.1,
                 norm_layer=nn.LayerNorm, ape=False, patch_norm=True,
                 use_checkpoint=False, norm_before_mlp='ln', **kwargs):
        super().__init__()

        self.num_classes = num_classes
        self.num_layers = len(depths)
        self.embed_dim = embed_dim
        self.ape = ape
        self.patch_norm = patch_norm
        self.num_features = int(embed_dim * 2 ** (self.num_layers - 1))
        self.mlp_ratio = mlp_ratio

        # split image into non-overlapping patches
        self.patch_embed = PatchEmbed(
            img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim,
            norm_layer=norm_layer if self.patch_norm else None)
        num_patches = self.patch_embed.num_patches
        patches_resolution = self.patch_embed.patches_resolution
        self.patches_resolution = patches_resolution

        # absolute position embedding
        if self.ape:
            self.absolute_pos_embed = nn.Parameter(torch.zeros(1, num_patches, embed_dim))
            trunc_normal_(self.absolute_pos_embed, std=.02)

        self.pos_drop = nn.Dropout(p=drop_rate) if drop_rate > 0. else nn.Identity()

        # stochastic depth
        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))]  # stochastic depth decay rule

        # build layers
        self.layers = nn.ModuleList()
        for i_layer in range(self.num_layers):
            layer = BasicLayer(dim=int(embed_dim * 2 ** i_layer),
                               input_resolution=(patches_resolution[0] // (2 ** i_layer),
                                                 patches_resolution[1] // (2 ** i_layer)),
                               depth=depths[i_layer],
                               num_heads=num_heads[i_layer],
                               window_size=window_size,
                               mlp_ratio=self.mlp_ratio,
                               qkv_bias=qkv_bias, qk_scale=qk_scale,
                               drop=drop_rate, attn_drop=attn_drop_rate,
                               drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])],
                               norm_layer=norm_layer,
                               downsample=PatchMerging if (i_layer < self.num_layers - 1) else None,
                               use_checkpoint=use_checkpoint,
                               norm_before_mlp=norm_before_mlp)
            self.layers.append(layer)

        self.norm = norm_layer(self.num_features)
        self.avgpool = nn.AdaptiveAvgPool1d(1)
        self.head = nn.Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity()

        self.apply(self._init_weights)

    def _init_weights(self, m):
        if isinstance(m, nn.Linear):
            trunc_normal_(m.weight, std=.02)
            if isinstance(m, nn.Linear) and m.bias is not None:
                nn.init.constant_(m.bias, 0)
        elif isinstance(m, nn.LayerNorm):
            nn.init.constant_(m.bias, 0)
            nn.init.constant_(m.weight, 1.0)

    @torch.jit.ignore
    def no_weight_decay(self):
        return {'absolute_pos_embed'}

    @torch.jit.ignore
    def no_weight_decay_keywords(self):
        return {'relative_position_bias_table'}

    @amp.half_function
    def forward_features(self, x):
        x = self.patch_embed(x)
        if self.ape:
            x = x + self.absolute_pos_embed
        x = self.pos_drop(x)

        for layer in self.layers:
            x = layer(x)

        x = self.norm(x)  # B L C
        x = self.avgpool(x.transpose(1, 2))  # B C 1
        x = torch.flatten(x, 1)
        return x

    @amp.half_function
    def forward(self, x):
        x = self.forward_features(x)
        x = self.head(x)
        return x

    def flops(self):
        flops = 0
        flops += self.patch_embed.flops()
        for i, layer in enumerate(self.layers):
            flops += layer.flops()
        flops += self.num_features * self.patches_resolution[0] * self.patches_resolution[1] // (2 ** self.num_layers)
        flops += self.num_features * self.num_classes
        return flops
